In general, medical imaging data generally assumes a stationary object. That is, it is assumed that the data has been acquired while a patient makes no motion. The motion includes both body movements and organ movements during the data acquisition. When the images are reconstructed based upon acquired data reflecting some motion, the reconstructed images substantially suffer from motion artifacts so that these images could lead the doctors to an inaccurate diagnosis.
One approach in improving the image quality is to identify certain portions of the acquired data that reflect the least amount of motion. Since the identified portions of the data have the least amount of motion, when the images are reconstructed from the projection data with the least motion, the reconstructed images are also least affected by the motion artifacts. This approach utilizes a motion index to identify certain data portions that had been acquired while the least amount of motion took place.
Some prior art motion indexes are based upon the Helgason-Ludwig consistency condition, complementary rays and a sinogram. Unfortunately, the prior art techniques are either inapplicable to different situations or inaccurate over various projection data sets. Thus, a new motion index remains to be desired for various applications and projection data sets.
A new motion index will be introduced for identifying a certain portion of data that has been acquired with a minimal amount of motion. Image reconstruction is implemented based upon the above identified data for substantially reducing motion artifacts in the reconstructed images in the hope that these improved images could prevent the doctors from making an inaccurate diagnosis.